What makes fish vulnerable to capture by hooks? A conceptual framework and a review of key determinants
Bibliographic record
Abstract
Abstract Considerable time and money are expended in the pursuit of catching fish with hooks (e.g., handlining, angling, longlining, trolling, drumlining) across the recreational, commercial and subsistence fishing sectors. The fish and other aquatic organisms (e.g., squid) that are captured are not a random sample of the population because external (e.g., turbidity) and underlying internal variables (e.g., morphology) contribute to variation in vulnerability to hooks. Vulnerability is the probability of capture for any given fish in a given location at a given time and mechanistically explains the population‐level catchability coefficient, which is a fundamental and usually time‐varying (i.e., dynamic) variable in fisheries science and stock assessment. The mechanistic drivers of individual vulnerability to capture are thus of interest to fishers by affecting catch rates, but are also of considerable importance to fisheries managers whenever hook‐and‐line‐generated data contribute to stock assessments. In this paper, individual vulnerability to hooks is conceptualized as a dynamic state, in which individual fish switch between vulnerable and invulnerable states as a function of three interdependent key processes: an individual fish's internal state, its encounter with the gear, and the characteristics of the encountered gear. We develop a new conceptual framework of “vulnerability,” summarize the major drivers of fish vulnerability, and conclude that fish vulnerability involves complex processes. To understand vulnerability, a shift to interdisciplinary research and the integration of ecophysiology, fish ecology, fisheries ecology and human movement ecology, facilitated by new technological developments, is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".